FtFoot : Follow the Footprints 👣

This repository contains the code (in PyTorch) for "Follow the Footprints: Self-supervised Traversability Estimation for Off-road Vehicle Navigation based on Geometric and Visual Cues" paper (ICRA 2024).

Environment

Step 1: Requirements

• CUDA 11.3
• cuDNN 8
• Ubuntu 20.04

Step 2: Create conda environment

conda create -n ftfoot python=3.8
conda activate ftfoot
pip install torch==1.7.0+cu110 torchvision==0.8.1+cu110 torchaudio==0.7.0 -f https://download.pytorch.org/whl/torch_stable.html
pip install -r requirements.txt

Step 3: Modify torch-encoding script

Modify the torch-encoding script by referring to this:

cd anaconda3/envs/ftfoot/lib/python3.8 # this path can be different, depends on your environment

1. fix the code in site-packages/encoding/nn/syncbn.py at about line 200
   from
   return syncbatchnorm(........).view(input_shape)
   to
   x, _, _=syncbatchnorm(........)
x=x.view(input_shape)
return x

2. fix the code site-packages/encoding/functions/syncbn.py at about line 102
   from
   ctx.save_for_backward(x,_ex,_exs,gamma,beta)
return y
   to
   ctx.save_for_backward(x,_ex,_exs,gamma,beta)
ctx.mark_non_differentiable(running_mean,running_var)
return y,running_mean,running_var

3. fix the code site-packages/encoding/functions/syncbn.py at about line 109
   from
   def backward(ctx,dz):
   to
   def backward(ctx,dz,_druning_mean,_druning_var):

Step 4: Install GFL

cd exts
python setup.py install

Data

RELLIS-3D

1. Download RELLIS-3D dataset. The folder structure is as follows.

RELLIS-3D
├── Rellis-3D
|   ├── 00000
|   |   ├── os1_cloud_node_kitti_bin
|   |   ├── pylon_camera_node
|   |   ├── calib.txt
|   |   ├── camera_info.txt
|   |   └── poses.txt    
|   ├── 00001
|   └── ..
└── Rellis_3D
    ├── 00000
    |   └── transforms.yaml
    ├── 00001
    └── ..

2. Prepare the data for training. Run:

sh ./data_prep/rellis_preproc.sh

3. The final folder structure is as follows.

RELLIS-3D
├── Rellis-3D
├── Rellis_3D
└── Rellis-3D-custom
    ├── 00000
    |   ├── foot_print
    |   ├── super_pixel # This is optional, but recommended for clear output!
    |   └── surface_normal
    ├── 00001
    └── ..

ORFD

1. Download ORFD dataset. The folder structure is as follows.

ORFD
└── Final_Dataset
    ├── training
    |   ├── calib
    |   ├── dense_depth
    |   ├── gt_image
    |   ├── image_data
    |   ├── lidar_data
    |   └── sparse_depth    
    ├── validation
    └── testing

2. This dataset has no pose data. Therefore, we need to estimate the pose data from the point cloud. We used PyICP-SLAM. Place the pose data under the directory.

ORFD
├── Final_Dataset
└── ORFD-custom
    ├── training
    |   └── pose
    |       └── pose_16197787.csv
    ├── validation
    └── testing

3. Prepare the data for training. Run:

sh ./data_prep/orfd_preproc.sh

4. The final folder structure is as follows.

ORFD
├── Final_Dataset
└── ORFD-custom
    ├── training
    |   ├── foot_print
    |   ├── pose
    |   ├── super_pixel
    |   └── surface_normal
    ├── validation
    └── testing

Traversability Estimation

Train

Set data_config/data_root in the train.yaml file and run:

python train.py configs/train.yaml

Test

Set data_config/data_root and resume_path in the test.yaml file and run:

python test.py configs/test.yaml

Path Plan

Plot global cost map

python ./plot_map/plot_map_rellis.py \
        --start_num 400 --end_num 900 \
        --save_rgb_img --save_valid_map \
        --cost_path ../outputs/prediction/your-ckpt-name

Generate path

python ./path_plan/path_plan.py \
        --start_num 400 --end_num 900 \
        --local_planner_type TRRTSTAR \
        --max_path_iter 1000 --max_extend_length 10 --bias_sampling \
        --cost_map_name /path/to/your-ckpt-name.png

Acknowledgements

• Our implementation of GFL is based on https://github.com/kakaxi314/GuideNet
• Our implementation of FSM is based on https://github.com/panzhiyi/URSS

If you have any questions, please contact Yurim Jeon at yurimjeon1892@gmail.com